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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 10529

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Dr. Nikos Koutsias

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Department of Environmental Engineering, University of Patras, G. Seferi 2, GR-30100 Agrinio, Greece
Interests: geoinformatics on wildland fires; natural disasters and landscape ecology; applied multivariate methods, geostatistics and point pattern analysis with special emphasis on spatio-temporal analysis of wildland fire ignition points; positional uncertainty and data modeling
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Dr. Alexandra Gemitzi

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Department of Environmental Engineering, Faculty of Engineering, Democritus University of Thrace, Xanthi, Greece
Interests: environmental modeling; remote sensing; groundwater-surface water interactions; GIS; climate change
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Dr. Sofia Bajocco

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Council for Agricultural Research and Economics (CREA), Research Centre for Engineering and Agro-Food Processing (CREA-IT), 00186 Rome, Italy
Interests: vegetation phenology dynamics; landscape disturbance; fire spatio-temporal behavior; land cover change processes; remotely sensed data analysis; geoprocessing techniques; multivariate statistical methods
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Special Issue Information

Dear Colleagues,

Satellite remote sensing can provide the necessary data to estimate phenology, an important element of landscape that can be useful, especially for climate and land use change assessments, at the global, continental, regional or even local scales. Phenology data can be used for the assessment of vegetation types distribution, carbon budget quantification, evaluation of year-to-year spatial and temporal variations of vegetation seasonality, and the dependence of these variations on environmental factors. Phenology data sets are also important for estimation of primary productivity, ecosystem healthiness and they also serve as input to land surface models. Given the plethora of free satellite missions and the available products, either those coming from medium-to-high spatial resolution sensors, e.g. Landsat and Sentinel, or from moderate resolution sensors, e.g. MODIS, time series methods are becoming very popular approaches. Moreover, the advent of hyperspectral missions, like PRISMA and Venµs, is opening new possibilities to estimate phenological parameters, allowing more precise spectral diagnostic and quantitative monitoring of vegetation phenology status over larger areas.

Remote sensing phenology captures broad scale phenological patterns with high degree of homogeneity and standardization offered by the nature of remote sensing data. Remotely sensed phenological data can be useful for numerous applications covering fields like forestry, agriculture, climate, hazards, oceanography and inland waters, drought severity, and wildfire risk. Under this perspective, in this special issue we expect and welcome high quality manuscripts on the assessment and use of satellite remote sensing time series data and satellite remote sensing phenological libraries that can be used in any scientific domain and field.

Assoc. Prof. Dr. Nikos Koutsias
Assoc. Prof. Dr. Alexandra Gemitzi
Dr. Sofia Bajocco
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Phenology
Remote Sensing
Seasonality
Forestry
Agriculture
Climate change
Hazards
Oceanography
Inland waters
Drought severity
Wildfire risk
Spatial
Temporal
Libraries
Time-series
MODIS
LANDSAT
SENTINEL

Published Papers (4 papers)

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Research

19 pages, 6526 KiB

Open AccessArticle

Snow Cover Phenology in Xinjiang Based on a Novel Method and MOD10A1 Data

by Qingxue Wang, Yonggang Ma and Junli Li

Remote Sens. 2023, 15(6), 1474; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15061474 - 07 Mar 2023

Cited by 4 | Viewed by 1361

Abstract

Using Earth observation to accurately extract snow phenology changes is of great significance for deepening the understanding of the ecological environment and hydrological process, agricultural and animal husbandry production, and high-quality development of the social economy in Xinjiang. Considering snow cover phenology based on MODIS product MOD10A1 data, this paper constructed a method for automatically extracting key phenological parameters in Xinjiang and calculated three key phenological parameters in Xinjiang from 2001 to 2020, including SCD (snow cover duration), SOD (snow onset date), and SED (snow end date). The daily data of four field camera observation points during an overlapping period from 2017 to 2019 were used to evaluate the snow cover phenological parameters extracted by MOD10A1, and the mean absolute error (MAE) and root mean square error (RMSE) values were 0.65 and 1.07, respectively. The results showed the following: 1. The spatiotemporal variation in snow phenology was highly altitude dependent. The mean gradients of SCD in the Altai Mountains, Tienshan Mountains, and Kunlun Mountains is 2.6, 2.1, and 1.2 d/100 m, respectively. The variation trend of snow phenology with latitude and longitude was mainly related to the topography of Xinjiang. Snow phenological parameters of different land-use types were different. The SCD values in wasteland were the lowest and the SED was the earliest, while forest land was the first to enter SOD accumulation. According to the study, the mean annual values of SCD, SOD, and SED were 25, 342 (8 December), and 51 (8 February) as day of year (DOY), respectively. The snow cover area was mainly distributed in the Altai Mountains, Junggar Basin, Tianshan Mountains, and Kunlun Mountains. 2. The variation trend and significance of snow cover phenological parameters in different regions are different, and the variation trend of snow cover phenological parameters in most regions of Xinjiang is non-significant. Full article

(This article belongs to the Special Issue Satellite Remote Sensing Phenological Libraries)

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21 pages, 6552 KiB

Open AccessArticle

Assessment of Fire Regimes and Post-Fire Evolution of Burned Areas with the Dynamic Time Warping Method on Time Series of Satellite Images—Setting the Methodological Framework in the Peloponnese, Greece

by Nikos Koutsias, Anastasia Karamitsou, Foula Nioti and Frank Coutelieris

Remote Sens. 2022, 14(20), 5237; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14205237 - 20 Oct 2022

Cited by 3 | Viewed by 1597

Abstract

Forest fires are considered to be an important part of numerous terrestrial ecosystems and vegetation types, being also a significant factor of ecosystem disruption. In this sense, fires play an important role in the structure and function of the ecosystems. Biomes are characterized by a specific type of fire regime, which is a synergy of the climate conditions and the characteristics of the vegetation types dominating each biome. The assessment of burned areas and the identification of the fire regimes can be implemented with freely available low- to high-resolution satellite data as those of Landsat and Sentinel-2. Moreover, the biomes are characterized by the phenology, a useful component for vegetation monitoring, especially when time series of satellite images are used. Both the identification of fire regime by reconstructing the fire history and the monitoring of the post-fire evolution of burned areas were studied with remote sensing methods. Specifically, the present paper is a pilot study implemented in a Mediterranean biome, aimed at establishing the methodological framework to (i) define fire regimes, (ii) characterize the phenological pattern of the vegetation (pre-fire situation) of the fire-affected areas, and (iii) compare the phenology of the recovered fire-affected areas with the corresponding one of the pre-fire situation. At the global level, based on MODIS fire perimeters, we found that fires are occurring at 70% in the tropical and subtropical grasslands, savannas, and shrublands, followed by fires at tropical and subtropical moist broadleaf forests by 7% and by fires at deserts and xeric shrublands by 6.5%. Full article

(This article belongs to the Special Issue Satellite Remote Sensing Phenological Libraries)

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14 pages, 2624 KiB

Open AccessArticle

Assessing the Accuracy of Forest Phenological Extraction from Sentinel-1 C-Band Backscatter Measurements in Deciduous and Coniferous Forests

by Yuxiang Ling, Shiwen Teng, Chao Liu, Jadunandan Dash, Harry Morris and Julio Pastor-Guzman

Remote Sens. 2022, 14(3), 674; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030674 - 31 Jan 2022

Cited by 10 | Viewed by 2863

Abstract

Satellite remote sensing is an important method for forest phenological studies at continental or global scales. Sentinel-1 (S1), a polar orbit satellite with a spatial resolution of 10 m, provides an opportunity to observe high-resolution forest phenology. The sensitivities of S1 C-band backscatter measurements to vegetation phenology, such as crops, meadows, and mixed forests, have been discussed, whereas their performance for different forest types has not yet been quantitatively assessed. It is necessary to evaluate accuracy before adapting S1 datasets in forest phenological studies. This study discusses the seasonal variations in S1 backscatter measurements and assesses the accuracy of S1-based forest phenological metrics in two types of typical forests: deciduous and coniferous. S1 C-band SAR dual-polarization backscatter measurements for the period 2017–2019 were used to extract forest phenology metrics using the Fourier transform (FT) and double logistic (DL) functions. Phenological metrics from the ground-based PhenoCam dataset were used for evaluation. The S1 backscatter VV-VH signal peaks for deciduous and coniferous forests occur in the winter and summer, respectively. The S1 backscatter could reasonably characterize the start of season (SOS) of deciduous forests, with R² values up to 0.8, whereas the R² values for coniferous forest SOS were less than 0.30. Moreover, the retrieved end of season (EOS) was less accurate than the SOS. The differences in accuracy of S1 backscatter phenological metrics between deciduous and coniferous forests can be explained by the differences in seasonal changes in their corresponding canopy structures. To conclude, S1 C-band backscatter has a reasonable performance when monitoring the SOS of deciduous broadleaf forests (R² = 0.8) and relatively poor performance when extracting EOS of deciduous broadleaf forests (R² = 0.25) or phenology of evergreen needleleaf forests (R² = 0.2). Full article

(This article belongs to the Special Issue Satellite Remote Sensing Phenological Libraries)

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17 pages, 5032 KiB

Open AccessArticle

Vegetation Growth Analysis of UNESCO World Heritage Hyrcanian Forests Using Multi-Sensor Optical Remote Sensing Data

by Suyash Khare, Hooman Latifi and Siddhartha Khare

Remote Sens. 2021, 13(19), 3965; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193965 - 03 Oct 2021

Cited by 15 | Viewed by 3397

Abstract

Freely available satellite data at Google Earth Engine (GEE) cloud platform enables vegetation phenology analysis across different scales very efficiently. We evaluated seasonal and annual phenology of the old-growth Hyrcanian forests (HF) of northern Iran covering an area of ca. 1.9 million ha, and also focused on 15 UNESCO World Heritage Sites. We extracted bi-weekly MODIS-NDVI between 2017 and 2020 in GEE, which was used to identify the range of NDVI between two temporal stages. Then, changes in phenology and growth were analyzed by Sentinel 2-derived Temporal Normalized Phenology Index. We modelled between seasonal phenology and growth by additionally considering elevation, surface temperature, and monthly precipitation. Results indicated considerable difference in onset of forests along the longitudinal gradient of the HF. Faster growth was observed in low- and uplands of the western zone, whereas it was lower in both the mid-elevations and the western outskirts. Longitudinal range was a major driver of vegetation growth, to which environmental factors also differently but significantly contributed (p < 0.0001) along the west-east gradient. Our study developed at GEE provides a benchmark to examine the effects of environmental parameters on the vegetation growth of HF, which cover mountainous areas with partly no or limited accessibility. Full article

(This article belongs to the Special Issue Satellite Remote Sensing Phenological Libraries)

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